Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations328
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.9 KiB
Average record size in memory184.0 B

Variable types

Numeric17
Categorical3
Boolean2

Alerts

Cloud3pm is highly overall correlated with Cloud9am and 2 other fieldsHigh correlation
Cloud9am is highly overall correlated with Cloud3pm and 2 other fieldsHigh correlation
Evaporation is highly overall correlated with Humidity9am and 4 other fieldsHigh correlation
Humidity3pm is highly overall correlated with Cloud3pm and 4 other fieldsHigh correlation
Humidity9am is highly overall correlated with Evaporation and 1 other fieldsHigh correlation
MaxTemp is highly overall correlated with Evaporation and 4 other fieldsHigh correlation
MinTemp is highly overall correlated with Evaporation and 5 other fieldsHigh correlation
Pressure3pm is highly overall correlated with MinTemp and 2 other fieldsHigh correlation
Pressure9am is highly overall correlated with MinTemp and 2 other fieldsHigh correlation
RISK_MM is highly overall correlated with RainTomorrowHigh correlation
RainToday is highly overall correlated with RainfallHigh correlation
RainTomorrow is highly overall correlated with RISK_MMHigh correlation
Rainfall is highly overall correlated with RainTodayHigh correlation
Sunshine is highly overall correlated with Cloud3pm and 3 other fieldsHigh correlation
Temp3pm is highly overall correlated with Evaporation and 4 other fieldsHigh correlation
Temp9am is highly overall correlated with Evaporation and 4 other fieldsHigh correlation
WindGustSpeed is highly overall correlated with Pressure9am and 1 other fieldsHigh correlation
WindSpeed3pm is highly overall correlated with WindGustSpeedHigh correlation
Rainfall has 234 (71.3%) zeros Zeros
Sunshine has 8 (2.4%) zeros Zeros
Cloud9am has 30 (9.1%) zeros Zeros
Cloud3pm has 6 (1.8%) zeros Zeros
RISK_MM has 236 (72.0%) zeros Zeros

Reproduction

Analysis started2024-11-09 07:35:27.209271
Analysis finished2024-11-09 07:35:48.163506
Duration20.95 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

MinTemp
Real number (ℝ)

High correlation 

Distinct170
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7429878
Minimum-5.3
Maximum20.9
Zeros1
Zeros (%)0.3%
Negative36
Negative (%)11.0%
Memory size5.1 KiB
2024-11-09T13:05:48.230594image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-5.3
5-th percentile-1.73
Q12.85
median7.9
Q312.8
95-th percentile16.565
Maximum20.9
Range26.2
Interquartile range (IQR)9.95

Descriptive statistics

Standard deviation5.9451992
Coefficient of variation (CV)0.7678172
Kurtosis-1.0678733
Mean7.7429878
Median Absolute Deviation (MAD)4.9
Skewness-0.073658037
Sum2539.7
Variance35.345394
MonotonicityNot monotonic
2024-11-09T13:05:48.327135image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4 6
 
1.8%
15.1 5
 
1.5%
2.3 5
 
1.5%
2.4 5
 
1.5%
8.3 5
 
1.5%
3.2 5
 
1.5%
0.5 5
 
1.5%
12.1 4
 
1.2%
-0.9 4
 
1.2%
15.4 4
 
1.2%
Other values (160) 280
85.4%
ValueCountFrequency (%)
-5.3 1
0.3%
-3.7 2
0.6%
-3.5 2
0.6%
-3.4 1
0.3%
-3.1 1
0.3%
-2.9 1
0.3%
-2.7 1
0.3%
-2.6 1
0.3%
-2.5 1
0.3%
-2.3 1
0.3%
ValueCountFrequency (%)
20.9 1
0.3%
19.9 1
0.3%
18.2 1
0.3%
18 1
0.3%
17.9 2
0.6%
17.6 1
0.3%
17.5 2
0.6%
17.2 2
0.6%
17.1 1
0.3%
17 1
0.3%

MaxTemp
Real number (ℝ)

High correlation 

Distinct175
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.897561
Minimum7.6
Maximum35.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:48.421713image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile11.3
Q115.5
median20.4
Q325.8
95-th percentile33.4
Maximum35.8
Range28.2
Interquartile range (IQR)10.3

Descriptive statistics

Standard deviation6.7073099
Coefficient of variation (CV)0.32096138
Kurtosis-0.7517544
Mean20.897561
Median Absolute Deviation (MAD)5.25
Skewness0.29735044
Sum6854.4
Variance44.988006
MonotonicityNot monotonic
2024-11-09T13:05:48.513744image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.8 5
 
1.5%
18.9 5
 
1.5%
11.6 5
 
1.5%
14.7 5
 
1.5%
24.3 4
 
1.2%
18 4
 
1.2%
18.5 4
 
1.2%
16.7 4
 
1.2%
20.9 4
 
1.2%
21 4
 
1.2%
Other values (165) 284
86.6%
ValueCountFrequency (%)
7.6 1
0.3%
8.4 1
0.3%
8.7 1
0.3%
8.8 1
0.3%
9.3 1
0.3%
9.6 1
0.3%
9.7 2
0.6%
10.4 1
0.3%
10.6 1
0.3%
10.7 2
0.6%
ValueCountFrequency (%)
35.8 1
 
0.3%
35.7 1
 
0.3%
35.2 1
 
0.3%
35 2
0.6%
34.9 1
 
0.3%
34.7 1
 
0.3%
34.2 2
0.6%
34.1 1
 
0.3%
33.9 2
0.6%
33.8 3
0.9%

Rainfall
Real number (ℝ)

High correlation  Zeros 

Distinct44
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4408537
Minimum0
Maximum39.8
Zeros234
Zeros (%)71.3%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:48.595294image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile8.72
Maximum39.8
Range39.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation4.2894268
Coefficient of variation (CV)2.9770038
Kurtosis27.611392
Mean1.4408537
Median Absolute Deviation (MAD)0
Skewness4.6798453
Sum472.6
Variance18.399182
MonotonicityNot monotonic
2024-11-09T13:05:48.690339image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 234
71.3%
0.2 15
 
4.6%
0.6 6
 
1.8%
0.4 5
 
1.5%
0.8 4
 
1.2%
4 3
 
0.9%
1 3
 
0.9%
1.8 3
 
0.9%
2 3
 
0.9%
3.4 3
 
0.9%
Other values (34) 49
 
14.9%
ValueCountFrequency (%)
0 234
71.3%
0.2 15
 
4.6%
0.4 5
 
1.5%
0.6 6
 
1.8%
0.8 4
 
1.2%
1 3
 
0.9%
1.2 3
 
0.9%
1.4 2
 
0.6%
1.6 2
 
0.6%
1.8 3
 
0.9%
ValueCountFrequency (%)
39.8 1
0.3%
25.8 1
0.3%
22.6 1
0.3%
19.8 1
0.3%
19.2 1
0.3%
18.8 1
0.3%
17.4 2
0.6%
16.2 2
0.6%
14.4 1
0.3%
12.2 1
0.3%

Evaporation
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.702439
Minimum0.2
Maximum13.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:48.779160image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.2
Q12.55
median4.4
Q36.6
95-th percentile9.53
Maximum13.8
Range13.6
Interquartile range (IQR)4.05

Descriptive statistics

Standard deviation2.6811826
Coefficient of variation (CV)0.5701685
Kurtosis-0.21605423
Mean4.702439
Median Absolute Deviation (MAD)2
Skewness0.59835187
Sum1542.4
Variance7.1887402
MonotonicityNot monotonic
2024-11-09T13:05:48.880566image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 18
 
5.5%
2.2 15
 
4.6%
2.6 13
 
4.0%
1.4 12
 
3.7%
1.6 12
 
3.7%
6.6 12
 
3.7%
3.4 11
 
3.4%
4.4 10
 
3.0%
2.4 10
 
3.0%
1.8 9
 
2.7%
Other values (45) 206
62.8%
ValueCountFrequency (%)
0.2 2
 
0.6%
0.6 4
 
1.2%
0.8 4
 
1.2%
1 2
 
0.6%
1.2 6
 
1.8%
1.4 12
3.7%
1.6 12
3.7%
1.8 9
2.7%
2 6
 
1.8%
2.2 15
4.6%
ValueCountFrequency (%)
13.8 1
 
0.3%
12.6 1
 
0.3%
12.4 1
 
0.3%
11.6 1
 
0.3%
11.4 1
 
0.3%
10.4 4
1.2%
10.2 1
 
0.3%
10 3
0.9%
9.6 4
1.2%
9.4 5
1.5%

Sunshine
Real number (ℝ)

High correlation  Zeros 

Distinct112
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.014939
Minimum0
Maximum13.6
Zeros8
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:48.981714image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q16
median8.75
Q310.7
95-th percentile12.6
Maximum13.6
Range13.6
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation3.5066459
Coefficient of variation (CV)0.43751373
Kurtosis-0.26692221
Mean8.014939
Median Absolute Deviation (MAD)2.25
Skewness-0.7371285
Sum2628.9
Variance12.296565
MonotonicityNot monotonic
2024-11-09T13:05:49.083869image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4 8
 
2.4%
0 8
 
2.4%
8.1 7
 
2.1%
11 7
 
2.1%
5.6 7
 
2.1%
9.9 6
 
1.8%
10.2 6
 
1.8%
8.9 6
 
1.8%
10.8 6
 
1.8%
11.1 6
 
1.8%
Other values (102) 261
79.6%
ValueCountFrequency (%)
0 8
2.4%
0.1 1
 
0.3%
0.2 1
 
0.3%
0.3 1
 
0.3%
0.4 2
 
0.6%
0.5 2
 
0.6%
0.6 3
 
0.9%
0.7 1
 
0.3%
0.8 3
 
0.9%
0.9 1
 
0.3%
ValueCountFrequency (%)
13.6 2
0.6%
13.5 1
 
0.3%
13.3 1
 
0.3%
13.2 2
0.6%
13.1 1
 
0.3%
13 4
1.2%
12.8 2
0.6%
12.7 3
0.9%
12.6 4
1.2%
12.5 2
0.6%

WindGustDir
Categorical

Distinct16
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
NW
64 
NNW
35 
E
34 
WNW
32 
ENE
29 
Other values (11)
134 

Length

Max length3
Median length2
Mean length2.1615854
Min length1

Characters and Unicode

Total characters709
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNW
2nd rowENE
3rd rowNW
4th rowNW
5th rowSSE

Common Values

ValueCountFrequency (%)
NW 64
19.5%
NNW 35
10.7%
E 34
10.4%
WNW 32
9.8%
ENE 29
8.8%
ESE 23
 
7.0%
S 21
 
6.4%
N 21
 
6.4%
NE 15
 
4.6%
W 15
 
4.6%
Other values (6) 39
11.9%

Length

2024-11-09T13:05:49.175128image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nw 64
19.5%
nnw 35
10.7%
e 34
10.4%
wnw 32
9.8%
ene 29
8.8%
ese 23
 
7.0%
s 21
 
6.4%
n 21
 
6.4%
ne 15
 
4.6%
w 15
 
4.6%
Other values (6) 39
11.9%

Most occurring characters

ValueCountFrequency (%)
N 245
34.6%
W 189
26.7%
E 183
25.8%
S 92
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 709
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 245
34.6%
W 189
26.7%
E 183
25.8%
S 92
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 709
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 245
34.6%
W 189
26.7%
E 183
25.8%
S 92
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 245
34.6%
W 189
26.7%
E 183
25.8%
S 92
 
13.0%

WindGustSpeed
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.396341
Minimum13
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:49.239548image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile20.7
Q131
median39
Q346
95-th percentile65
Maximum98
Range85
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.132176
Coefficient of variation (CV)0.32508331
Kurtosis1.5948709
Mean40.396341
Median Absolute Deviation (MAD)8
Skewness0.89156193
Sum13250
Variance172.45405
MonotonicityNot monotonic
2024-11-09T13:05:49.318576image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
39 31
 
9.5%
41 29
 
8.8%
31 25
 
7.6%
35 22
 
6.7%
30 21
 
6.4%
46 21
 
6.4%
33 19
 
5.8%
43 16
 
4.9%
44 15
 
4.6%
48 15
 
4.6%
Other values (25) 114
34.8%
ValueCountFrequency (%)
13 1
 
0.3%
15 2
 
0.6%
17 6
 
1.8%
20 8
 
2.4%
22 7
 
2.1%
24 4
 
1.2%
26 9
 
2.7%
28 13
4.0%
30 21
6.4%
31 25
7.6%
ValueCountFrequency (%)
98 1
 
0.3%
85 1
 
0.3%
83 1
 
0.3%
80 1
 
0.3%
78 2
 
0.6%
76 2
 
0.6%
70 4
1.2%
69 1
 
0.3%
67 1
 
0.3%
65 6
1.8%

WindDir9am
Categorical

Distinct16
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
SE
47 
SSE
38 
NNW
36 
N
30 
NW
30 
Other values (11)
147 

Length

Max length3
Median length2
Mean length2.2195122
Min length1

Characters and Unicode

Total characters728
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSW
2nd rowE
3rd rowN
4th rowWNW
5th rowSSE

Common Values

ValueCountFrequency (%)
SE 47
14.3%
SSE 38
11.6%
NNW 36
11.0%
N 30
9.1%
NW 30
9.1%
ESE 29
8.8%
S 26
7.9%
E 20
6.1%
SSW 17
 
5.2%
WNW 16
 
4.9%
Other values (6) 39
11.9%

Length

2024-11-09T13:05:49.397240image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se 47
14.3%
sse 38
11.6%
nnw 36
11.0%
n 30
9.1%
nw 30
9.1%
ese 29
8.8%
s 26
7.9%
e 20
6.1%
ssw 17
 
5.2%
wnw 16
 
4.9%
Other values (6) 39
11.9%

Most occurring characters

ValueCountFrequency (%)
S 224
30.8%
E 190
26.1%
N 174
23.9%
W 140
19.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 728
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 224
30.8%
E 190
26.1%
N 174
23.9%
W 140
19.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 728
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 224
30.8%
E 190
26.1%
N 174
23.9%
W 140
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 224
30.8%
E 190
26.1%
N 174
23.9%
W 140
19.2%

WindDir3pm
Categorical

Distinct16
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
NW
55 
WNW
53 
NNW
39 
N
27 
ESE
26 
Other values (11)
128 

Length

Max length3
Median length2.5
Mean length2.2530488
Min length1

Characters and Unicode

Total characters739
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNW
2nd rowW
3rd rowNNE
4th rowW
5th rowESE

Common Values

ValueCountFrequency (%)
NW 55
16.8%
WNW 53
16.2%
NNW 39
11.9%
N 27
8.2%
ESE 26
7.9%
W 24
7.3%
E 17
 
5.2%
NNE 14
 
4.3%
NE 13
 
4.0%
S 13
 
4.0%
Other values (6) 47
14.3%

Length

2024-11-09T13:05:49.629571image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nw 55
16.8%
wnw 53
16.2%
nnw 39
11.9%
n 27
8.2%
ese 26
7.9%
w 24
7.3%
e 17
 
5.2%
nne 14
 
4.3%
ne 13
 
4.0%
s 13
 
4.0%
Other values (6) 47
14.3%

Most occurring characters

ValueCountFrequency (%)
N 264
35.7%
W 253
34.2%
E 134
18.1%
S 88
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 739
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 264
35.7%
W 253
34.2%
E 134
18.1%
S 88
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 739
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 264
35.7%
W 253
34.2%
E 134
18.1%
S 88
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 264
35.7%
W 253
34.2%
E 134
18.1%
S 88
 
11.9%

WindSpeed9am
Real number (ℝ)

Distinct21
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.414634
Minimum2
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:49.708923image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q16
median7
Q313
95-th percentile28
Maximum41
Range39
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.8115436
Coefficient of variation (CV)0.75005454
Kurtosis1.5352321
Mean10.414634
Median Absolute Deviation (MAD)3
Skewness1.430616
Sum3416
Variance61.020213
MonotonicityNot monotonic
2024-11-09T13:05:49.778907image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6 75
22.9%
7 52
15.9%
2 34
10.4%
9 28
 
8.5%
4 25
 
7.6%
11 19
 
5.8%
13 17
 
5.2%
20 14
 
4.3%
15 13
 
4.0%
17 8
 
2.4%
Other values (11) 43
13.1%
ValueCountFrequency (%)
2 34
10.4%
4 25
 
7.6%
6 75
22.9%
7 52
15.9%
9 28
 
8.5%
11 19
 
5.8%
13 17
 
5.2%
15 13
 
4.0%
17 8
 
2.4%
19 7
 
2.1%
ValueCountFrequency (%)
41 1
 
0.3%
39 1
 
0.3%
35 1
 
0.3%
33 1
 
0.3%
31 6
1.8%
30 5
1.5%
28 4
1.2%
26 6
1.8%
24 7
2.1%
22 4
1.2%

WindSpeed3pm
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.185976
Minimum4
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:49.852857image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q111
median17
Q324
95-th percentile33
Maximum52
Range48
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.9267586
Coefficient of variation (CV)0.49085949
Kurtosis0.26558799
Mean18.185976
Median Absolute Deviation (MAD)7
Skewness0.64610625
Sum5965
Variance79.68702
MonotonicityNot monotonic
2024-11-09T13:05:49.930744image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11 29
 
8.8%
9 27
 
8.2%
13 27
 
8.2%
7 26
 
7.9%
20 25
 
7.6%
24 21
 
6.4%
17 21
 
6.4%
19 20
 
6.1%
28 19
 
5.8%
15 19
 
5.8%
Other values (14) 94
28.7%
ValueCountFrequency (%)
4 2
 
0.6%
6 17
5.2%
7 26
7.9%
9 27
8.2%
11 29
8.8%
13 27
8.2%
15 19
5.8%
17 21
6.4%
19 20
6.1%
20 25
7.6%
ValueCountFrequency (%)
52 1
 
0.3%
50 1
 
0.3%
48 1
 
0.3%
41 2
 
0.6%
39 1
 
0.3%
37 3
 
0.9%
35 3
 
0.9%
33 7
2.1%
31 8
2.4%
30 14
4.3%

Humidity9am
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.10061
Minimum36
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:50.012074image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile47
Q163
median71
Q380
95-th percentile93
Maximum99
Range63
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.983367
Coefficient of variation (CV)0.18260557
Kurtosis-0.14562214
Mean71.10061
Median Absolute Deviation (MAD)8
Skewness-0.091998537
Sum23321
Variance168.56783
MonotonicityNot monotonic
2024-11-09T13:05:50.096370image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 17
 
5.2%
70 16
 
4.9%
74 14
 
4.3%
60 12
 
3.7%
76 12
 
3.7%
72 11
 
3.4%
80 11
 
3.4%
68 11
 
3.4%
82 11
 
3.4%
66 10
 
3.0%
Other values (50) 203
61.9%
ValueCountFrequency (%)
36 1
 
0.3%
38 1
 
0.3%
41 1
 
0.3%
42 1
 
0.3%
43 3
0.9%
44 5
1.5%
45 2
 
0.6%
46 1
 
0.3%
47 3
0.9%
48 1
 
0.3%
ValueCountFrequency (%)
99 6
1.8%
97 2
 
0.6%
96 1
 
0.3%
95 4
1.2%
94 3
 
0.9%
93 2
 
0.6%
92 8
2.4%
91 3
 
0.9%
90 4
1.2%
89 4
1.2%

Humidity3pm
Real number (ℝ)

High correlation 

Distinct71
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.003049
Minimum13
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:50.177796image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile20
Q132
median42.5
Q354
95-th percentile74
Maximum93
Range80
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.605975
Coefficient of variation (CV)0.37738237
Kurtosis-0.11806161
Mean44.003049
Median Absolute Deviation (MAD)11.5
Skewness0.53973195
Sum14433
Variance275.7584
MonotonicityNot monotonic
2024-11-09T13:05:50.272109image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 13
 
4.0%
48 12
 
3.7%
35 10
 
3.0%
49 10
 
3.0%
51 10
 
3.0%
42 9
 
2.7%
25 9
 
2.7%
44 9
 
2.7%
43 9
 
2.7%
28 9
 
2.7%
Other values (61) 228
69.5%
ValueCountFrequency (%)
13 1
 
0.3%
14 1
 
0.3%
15 4
1.2%
16 3
0.9%
17 1
 
0.3%
18 4
1.2%
20 5
1.5%
21 1
 
0.3%
22 6
1.8%
23 2
 
0.6%
ValueCountFrequency (%)
93 1
 
0.3%
90 1
 
0.3%
88 1
 
0.3%
86 3
0.9%
85 2
0.6%
82 1
 
0.3%
80 1
 
0.3%
79 1
 
0.3%
78 3
0.9%
76 2
0.6%

Pressure9am
Real number (ℝ)

High correlation 

Distinct183
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1019.35
Minimum996.5
Maximum1035.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:50.366734image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum996.5
5-th percentile1007.635
Q11014.8
median1019.75
Q31024.3
95-th percentile1029.36
Maximum1035.7
Range39.2
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation6.7152435
Coefficient of variation (CV)0.0065877702
Kurtosis-0.088998044
Mean1019.35
Median Absolute Deviation (MAD)4.7
Skewness-0.33164729
Sum334346.8
Variance45.094495
MonotonicityNot monotonic
2024-11-09T13:05:50.460928image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025.7 6
 
1.8%
1023.2 6
 
1.8%
1018 5
 
1.5%
1017.4 5
 
1.5%
1020 4
 
1.2%
1027.8 4
 
1.2%
1016.8 4
 
1.2%
1021 4
 
1.2%
1020.8 4
 
1.2%
1025.2 4
 
1.2%
Other values (173) 282
86.0%
ValueCountFrequency (%)
996.5 1
0.3%
999.4 1
0.3%
1002.1 1
0.3%
1003.2 1
0.3%
1004 1
0.3%
1004.9 1
0.3%
1005.1 1
0.3%
1005.5 1
0.3%
1006.3 2
0.6%
1006.6 1
0.3%
ValueCountFrequency (%)
1035.7 1
0.3%
1034.3 1
0.3%
1033.6 1
0.3%
1033.5 1
0.3%
1032.2 2
0.6%
1032.1 1
0.3%
1031 1
0.3%
1030.5 2
0.6%
1030.4 1
0.3%
1030.3 1
0.3%

Pressure3pm
Real number (ℝ)

High correlation 

Distinct184
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1016.5308
Minimum996.8
Maximum1033.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:50.562080image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum996.8
5-th percentile1006.035
Q11012.4
median1016.9
Q31021.125
95-th percentile1026
Maximum1033.2
Range36.4
Interquartile range (IQR)8.725

Descriptive statistics

Standard deviation6.4697742
Coefficient of variation (CV)0.006364563
Kurtosis0.00091395541
Mean1016.5308
Median Absolute Deviation (MAD)4.45
Skewness-0.26750581
Sum333422.1
Variance41.857979
MonotonicityNot monotonic
2024-11-09T13:05:50.650352image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1019.3 7
 
2.1%
1015 4
 
1.2%
1013.7 4
 
1.2%
1016.8 4
 
1.2%
1014.1 4
 
1.2%
1019.1 4
 
1.2%
1018.6 4
 
1.2%
1019.2 4
 
1.2%
1014.9 4
 
1.2%
1006.5 4
 
1.2%
Other values (174) 285
86.9%
ValueCountFrequency (%)
996.8 1
0.3%
997.5 1
0.3%
997.7 1
0.3%
998.9 1
0.3%
1001.3 1
0.3%
1001.5 1
0.3%
1001.8 1
0.3%
1003 1
0.3%
1003.3 1
0.3%
1004 1
0.3%
ValueCountFrequency (%)
1033.2 1
0.3%
1031.9 1
0.3%
1031.7 1
0.3%
1031.1 1
0.3%
1030 1
0.3%
1029.6 1
0.3%
1028 1
0.3%
1027.7 1
0.3%
1027.4 2
0.6%
1027.2 1
0.3%

Cloud9am
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9054878
Minimum0
Maximum8
Zeros30
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:50.728234image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.9749567
Coefficient of variation (CV)0.76173755
Kurtosis-1.7198825
Mean3.9054878
Median Absolute Deviation (MAD)3
Skewness0.078980147
Sum1281
Variance8.8503673
MonotonicityNot monotonic
2024-11-09T13:05:50.806865image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 104
31.7%
7 75
22.9%
8 39
 
11.9%
0 30
 
9.1%
6 22
 
6.7%
5 20
 
6.1%
2 15
 
4.6%
3 14
 
4.3%
4 9
 
2.7%
ValueCountFrequency (%)
0 30
 
9.1%
1 104
31.7%
2 15
 
4.6%
3 14
 
4.3%
4 9
 
2.7%
5 20
 
6.1%
6 22
 
6.7%
7 75
22.9%
8 39
 
11.9%
ValueCountFrequency (%)
8 39
 
11.9%
7 75
22.9%
6 22
 
6.7%
5 20
 
6.1%
4 9
 
2.7%
3 14
 
4.3%
2 15
 
4.6%
1 104
31.7%
0 30
 
9.1%

Cloud3pm
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum0
Maximum8
Zeros6
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:50.870256image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median4
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.6521009
Coefficient of variation (CV)0.66302522
Kurtosis-1.6219622
Mean4
Median Absolute Deviation (MAD)3
Skewness0.080163124
Sum1312
Variance7.0336391
MonotonicityNot monotonic
2024-11-09T13:05:50.949262image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 96
29.3%
7 75
22.9%
2 34
 
10.4%
6 29
 
8.8%
5 25
 
7.6%
8 23
 
7.0%
3 20
 
6.1%
4 20
 
6.1%
0 6
 
1.8%
ValueCountFrequency (%)
0 6
 
1.8%
1 96
29.3%
2 34
 
10.4%
3 20
 
6.1%
4 20
 
6.1%
5 25
 
7.6%
6 29
 
8.8%
7 75
22.9%
8 23
 
7.0%
ValueCountFrequency (%)
8 23
 
7.0%
7 75
22.9%
6 29
 
8.8%
5 25
 
7.6%
4 20
 
6.1%
3 20
 
6.1%
2 34
 
10.4%
1 96
29.3%
0 6
 
1.8%

Temp9am
Real number (ℝ)

High correlation 

Distinct168
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.815549
Minimum0.1
Maximum24.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:51.044500image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile3.8
Q18.175
median13.5
Q317.2
95-th percentile21.465
Maximum24.7
Range24.6
Interquartile range (IQR)9.025

Descriptive statistics

Standard deviation5.5425214
Coefficient of variation (CV)0.43248412
Kurtosis-0.88100776
Mean12.815549
Median Absolute Deviation (MAD)4.45
Skewness-0.094301244
Sum4203.5
Variance30.719543
MonotonicityNot monotonic
2024-11-09T13:05:51.129105image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 6
 
1.8%
15.8 5
 
1.5%
15.3 5
 
1.5%
17.4 5
 
1.5%
7.9 5
 
1.5%
5.5 4
 
1.2%
8.8 4
 
1.2%
6.2 4
 
1.2%
13.5 4
 
1.2%
15.7 4
 
1.2%
Other values (158) 282
86.0%
ValueCountFrequency (%)
0.1 1
0.3%
0.8 1
0.3%
1 1
0.3%
1.2 1
0.3%
1.4 2
0.6%
1.8 1
0.3%
2.1 1
0.3%
2.6 1
0.3%
2.7 1
0.3%
3 1
0.3%
ValueCountFrequency (%)
24.7 1
0.3%
24.5 1
0.3%
23.8 1
0.3%
23.6 1
0.3%
23.4 1
0.3%
23 2
0.6%
22.8 1
0.3%
22.5 1
0.3%
22.4 1
0.3%
22.2 2
0.6%

Temp3pm
Real number (ℝ)

High correlation 

Distinct190
Distinct (%)57.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.556402
Minimum5.1
Maximum34.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:51.212398image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile9.97
Q114.5
median18.85
Q324.225
95-th percentile31.765
Maximum34.5
Range29.4
Interquartile range (IQR)9.725

Descriptive statistics

Standard deviation6.6443111
Coefficient of variation (CV)0.3397512
Kurtosis-0.6703703
Mean19.556402
Median Absolute Deviation (MAD)4.95
Skewness0.26394352
Sum6414.5
Variance44.14687
MonotonicityNot monotonic
2024-11-09T13:05:51.290644image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1 5
 
1.5%
28.6 4
 
1.2%
18.6 4
 
1.2%
27 4
 
1.2%
15.4 4
 
1.2%
16.3 4
 
1.2%
12.3 4
 
1.2%
16.2 4
 
1.2%
16.6 4
 
1.2%
26.3 4
 
1.2%
Other values (180) 287
87.5%
ValueCountFrequency (%)
5.1 1
0.3%
5.7 1
0.3%
6.9 1
0.3%
7.1 1
0.3%
7.3 1
0.3%
7.8 1
0.3%
8 1
0.3%
8.1 1
0.3%
8.2 1
0.3%
8.3 1
0.3%
ValueCountFrequency (%)
34.5 1
0.3%
34.3 1
0.3%
34.1 1
0.3%
34 1
0.3%
33.6 1
0.3%
33.1 1
0.3%
32.8 1
0.3%
32.7 2
0.6%
32.3 1
0.3%
32.2 2
0.6%

RainToday
Boolean

High correlation 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
False
267 
True
61 
ValueCountFrequency (%)
False 267
81.4%
True 61
 
18.6%
2024-11-09T13:05:51.371341image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

RISK_MM
Real number (ℝ)

High correlation  Zeros 

Distinct44
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.422561
Minimum0
Maximum39.8
Zeros236
Zeros (%)72.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-11-09T13:05:51.447611image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile8.72
Maximum39.8
Range39.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation4.2340231
Coefficient of variation (CV)2.9763385
Kurtosis28.557778
Mean1.422561
Median Absolute Deviation (MAD)0
Skewness4.7230787
Sum466.6
Variance17.926951
MonotonicityNot monotonic
2024-11-09T13:05:51.550858image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 236
72.0%
0.2 14
 
4.3%
0.4 5
 
1.5%
0.8 5
 
1.5%
0.6 5
 
1.5%
1.8 3
 
0.9%
2 3
 
0.9%
6.6 3
 
0.9%
5.2 3
 
0.9%
3.4 3
 
0.9%
Other values (34) 48
 
14.6%
ValueCountFrequency (%)
0 236
72.0%
0.2 14
 
4.3%
0.4 5
 
1.5%
0.6 5
 
1.5%
0.8 5
 
1.5%
1 3
 
0.9%
1.2 3
 
0.9%
1.4 2
 
0.6%
1.6 2
 
0.6%
1.8 3
 
0.9%
ValueCountFrequency (%)
39.8 1
0.3%
25.8 1
0.3%
22.6 1
0.3%
19.8 1
0.3%
18.8 1
0.3%
17.4 2
0.6%
16.2 2
0.6%
14.4 1
0.3%
13.2 1
0.3%
12.2 1
0.3%

RainTomorrow
Boolean

High correlation 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
False
268 
True
60 
ValueCountFrequency (%)
False 268
81.7%
True 60
 
18.3%
2024-11-09T13:05:51.618140image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Interactions

2024-11-09T13:05:46.530338image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:28.117752image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:29.318246image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:30.335062image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:31.467295image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:32.782439image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:33.971115image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:35.087509image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:36.350390image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-11-09T13:05:34.739709image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:36.017149image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:37.112844image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:38.199944image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:39.320745image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:40.587082image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:41.752407image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:42.882609image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:44.064705image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:45.148728image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:46.205519image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:47.547200image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:29.084481image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:30.102025image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:31.189610image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:32.500724image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:33.683867image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:34.815704image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:36.099487image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:37.177386image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:38.265573image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:39.385588image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:40.667295image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:41.817292image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:42.930975image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:44.149543image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:45.215515image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:46.272743image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:47.630160image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:29.151763image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:30.169354image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:31.266466image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:32.582861image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:33.758299image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:34.886369image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:36.168348image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:37.244834image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:38.333000image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:39.449334image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:40.740065image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:41.891715image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:43.008314image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:44.230201image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:45.289097image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:46.352872image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:47.698516image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:29.201658image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:30.217944image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:31.332787image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:32.634926image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:33.832361image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:34.934652image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:36.236327image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:37.311213image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:38.401186image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:39.498793image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:40.802572image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:41.962534image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:43.065137image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:44.280294image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:45.359917image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:46.413731image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:47.764247image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:29.251473image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:30.267345image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:31.400970image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:32.712080image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:33.901424image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:35.016494image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:36.300889image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:37.375475image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:38.491136image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:39.566111image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:40.848944image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:42.032447image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:43.263772image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:44.348310image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:45.414115image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-11-09T13:05:46.483618image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-11-09T13:05:51.676533image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Cloud3pmCloud9amEvaporationHumidity3pmHumidity9amMaxTempMinTempPressure3pmPressure9amRISK_MMRainTodayRainTomorrowRainfallSunshineTemp3pmTemp9amWindDir3pmWindDir9amWindGustDirWindGustSpeedWindSpeed3pmWindSpeed9am
Cloud3pm1.0000.551-0.1060.5060.274-0.1090.153-0.156-0.1540.4470.2130.3910.190-0.638-0.1410.0730.0570.0000.0990.0040.0060.004
Cloud9am0.5511.000-0.1400.5820.418-0.1790.222-0.141-0.1800.3570.3110.2920.313-0.714-0.2010.0240.0460.0620.1190.0100.0020.221
Evaporation-0.106-0.1401.000-0.419-0.5120.6920.642-0.403-0.3840.0160.0000.000-0.1110.3950.6760.7030.0000.0230.1350.2900.0030.033
Humidity3pm0.5060.582-0.4191.0000.478-0.526-0.0440.009-0.0820.3690.3190.3510.390-0.741-0.564-0.2630.0830.0000.000-0.0660.0530.271
Humidity9am0.2740.418-0.5120.4781.000-0.301-0.1640.1300.1260.2300.1760.2230.240-0.527-0.295-0.3660.0000.0000.000-0.363-0.234-0.205
MaxTemp-0.109-0.1790.692-0.526-0.3011.0000.754-0.380-0.2880.0320.0000.146-0.1290.4780.9910.8780.0710.0910.1300.082-0.253-0.258
MinTemp0.1530.2220.642-0.044-0.1640.7541.000-0.507-0.5080.2500.1710.2950.2070.0780.7250.9130.0930.0720.1790.204-0.1240.110
Pressure3pm-0.156-0.141-0.4030.0090.130-0.380-0.5071.0000.968-0.3950.2780.398-0.301-0.058-0.356-0.5100.1200.1010.118-0.497-0.307-0.160
Pressure9am-0.154-0.180-0.384-0.0820.126-0.288-0.5080.9681.000-0.3650.3360.377-0.383-0.013-0.260-0.4670.0830.1350.113-0.526-0.347-0.268
RISK_MM0.4470.3570.0160.3690.2300.0320.250-0.395-0.3651.0000.1410.7290.257-0.4460.0030.1830.0540.1180.0510.1910.0300.074
RainToday0.2130.3110.0000.3190.1760.0000.1710.2780.3360.1411.0000.1600.7210.1620.0000.0450.1640.1810.0630.2050.0000.281
RainTomorrow0.3910.2920.0000.3510.2230.1460.2950.3980.3770.7290.1601.0000.1690.3640.1270.1590.0000.1640.2520.3370.1560.137
Rainfall0.1900.313-0.1110.3900.240-0.1290.207-0.301-0.3830.2570.7210.1691.000-0.238-0.1390.0340.1200.0000.0000.1680.1230.254
Sunshine-0.638-0.7140.395-0.741-0.5270.4780.078-0.058-0.013-0.4460.1620.364-0.2381.0000.4960.2670.0830.0000.0740.091-0.003-0.153
Temp3pm-0.141-0.2010.676-0.564-0.2950.9910.725-0.356-0.2600.0030.0000.127-0.1390.4961.0000.8550.0840.0000.1420.050-0.271-0.278
Temp9am0.0730.0240.703-0.263-0.3660.8780.913-0.510-0.4670.1830.0450.1590.0340.2670.8551.0000.0770.0000.1390.234-0.0910.006
WindDir3pm0.0570.0460.0000.0830.0000.0710.0930.1200.0830.0540.1640.0000.1200.0830.0840.0771.0000.1180.2780.0520.1510.109
WindDir9am0.0000.0620.0230.0000.0000.0910.0720.1010.1350.1180.1810.1640.0000.0000.0000.0000.1181.0000.1450.1320.1410.154
WindGustDir0.0990.1190.1350.0000.0000.1300.1790.1180.1130.0510.0630.2520.0000.0740.1420.1390.2780.1451.0000.1330.1580.112
WindGustSpeed0.0040.0100.290-0.066-0.3630.0820.204-0.497-0.5260.1910.2050.3370.1680.0910.0500.2340.0520.1320.1331.0000.6680.434
WindSpeed3pm0.0060.0020.0030.053-0.234-0.253-0.124-0.307-0.3470.0300.0000.1560.123-0.003-0.271-0.0910.1510.1410.1580.6681.0000.385
WindSpeed9am0.0040.2210.0330.271-0.205-0.2580.110-0.160-0.2680.0740.2810.1370.254-0.153-0.2780.0060.1090.1540.1120.4340.3851.000

Missing values

2024-11-09T13:05:47.881271image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-09T13:05:48.079696image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRISK_MMRainTomorrow
08.024.30.03.46.3NW30.0SWNW6.02068291019.71015.07714.423.6No3.6Yes
114.026.93.64.49.7ENE39.0EW4.01780361012.41008.45317.525.7Yes3.6Yes
213.723.43.65.83.3NW85.0NNNE6.0682691009.51007.28715.420.2Yes39.8Yes
313.315.539.87.29.1NW54.0WNWW30.02462561005.51007.02713.514.1Yes2.8Yes
47.616.12.85.610.6SSE50.0SSEESE20.02868491018.31018.57711.115.4Yes0.0No
56.216.90.05.88.2SE44.0SEE20.02470571023.81021.77510.914.8No0.2No
66.118.20.24.28.4SE43.0SEESE19.02663471024.61022.24612.417.3No0.0No
78.317.00.05.64.6E41.0SEE11.02465571026.21024.26712.115.5No0.0No
88.819.50.04.04.1S48.0EENE19.01770481026.11022.77714.118.9No16.2Yes
98.422.816.25.47.7E31.0SESE7.0682321024.11020.77113.321.7Yes0.0No
MinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRISK_MMRainTomorrow
3559.020.60.09.06.2ENE39.0SSW11.01154281022.31018.67511.418.5No0.8No
3563.415.00.84.811.7S70.0SS35.03743241023.41023.1158.314.3No0.0No
3573.218.00.07.412.2SSE48.0SSES26.01547251026.61022.8129.116.3No0.0No
3580.920.70.05.48.4NNW39.0SSEN2.01771291023.21018.4389.419.1No0.0No
3593.325.50.05.210.8N43.0NNNW4.01957161018.81014.60312.024.8No0.0No
3619.030.70.07.612.1NNW76.0SSENW7.05038151016.11010.81320.430.0No0.0No
3627.128.40.011.612.7N48.0NNWNNW2.01945221020.01016.90117.228.2No0.0No
36312.519.90.08.45.3ESE43.0ENEENE11.0963471024.01022.83214.518.3No0.0No
36412.526.90.05.07.1NW46.0SSWWNW6.02869391021.01016.26715.825.9No0.0No
36512.330.20.06.012.6NW78.0NWWNW31.03543131009.61009.21123.828.6No0.0No